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Much previous research has problematised the use of a binary urban-rural distinction to describe human settlement patterns in and around cities. Peri-urban zones, on the edge of urban settlements, are significant both in sheer magnitude of human population and in terms of being home to vulnerable populations with high rates of poverty. This paper presents a framework that conceptualises rural25 urban transition through the prism of shifts in natural, engineered and institutional infrastructure, in order to explain the processes of rapid change and the dip in service provision often found in peri-urban areas in the Global South. We draw on examples related to the provision of water and sanitation to illustrate the theory and discuss its implications for future research on the peri-urban. A research agenda is set out that emphasises the importance of studying early warning signs of service dips using systems theory concepts such as flickering and critical slowing down. Through such approaches, research can better predict and explain what we call peri-urban turbulence and inform the development of mitigation strategies to reduce the vulnerabilities that peri-urban residents too often face during periods of rural-urban transition.

The demands of the modern world are increasingly responsible for causing psychological burdens and bringing adverse impacts on our mental health. As a result, neural conversational agents with empathetic responding and distress management capabilities have recently gained popularity. However, existing end-to-end empathetic conversational agents often generate generic and repetitive empathetic statements such as "I am sorry to hear that", which fail to convey specificity to a given situation. Due to the lack of controllability in such models, they also impose the risk of generating toxic responses. Chatbots leveraging reasoning over knowledge graphs is seen as an efficient and fail-safe solution over end-to-end models. However, such resources are limited in the context of emotional distress. To address this, we introduce HEAL, a knowledge graph developed based on 1M distress narratives and their corresponding consoling responses curated from Reddit. It consists of 22K nodes identifying different types of stressors, speaker expectations, responses, and feedback types associated with distress dialogues and forms 104K connections between different types of nodes. Each node is associated with one of 41 affective states. Statistical and visual analysis conducted on HEAL reveals emotional dynamics between speakers and listeners in distress-oriented conversations and identifies useful response patterns leading to emotional relief. Automatic and human evaluation experiments show that HEAL's responses are more diverse, empathetic, and reliable compared to the baselines.

Visual impairment can affect a student's ability to learn since their concept development when interacting with educational material is being limited. Learning activities based on images and visually rich content are mainstream learning methods, where facilitating students with visual impairments for engaged learning can be challenging. For comic books, which have shown promising results in engaged student learning, this problem is more severe. To overcome this challenge, this research presents a novel voice synthesised learning method to reduce the gap between the learning experience of a student with visual impairments compared to a mainstream learning activity. Utilising comic books, the proposed technique and the tool developed extracts semantic content, stores them in a database, and generates an audio stream in multiple languages on the user's demand. To assess the usability of the system, a survey for a selected set of students with visual impairments was carried out. The results showed a mean rating of 5.76 out of 7 for the Informative Interest. Furthermore, a concept-mapping approach was used to analyse the feedback given through the open-ended questions. From the analysis, key concepts with an emphasis on positive emotions, willingness to try again, and features to be improved, were identified.

Digitisation of comic books would play a crucial role in identifying new areas in which digital comics can be used. Currently, existing systems in this domain lack the capacity to achieve complete digitisation. Digitisation requires a thorough analysis of the semantic content within comic books. This can be further sub-categorised as detection and identification of comic book characters, extraction and analysis of panels as well as texts, derivation of associations between characters and speech balloons, and analysis of different styles of reading. This paper provides an overview of using several object-detection models to detect semantic content in comics. This analysis showed that, under the constraint of limited computational capacity, YOLOv3 was the best-suited model out of the models evaluated. A study of text extraction and recognition using Optical Character Recognition, a method for determining associable speech balloons, as well as a distance-based approach for associations between characters and speech balloons are also presented here. This association method provides an increased accuracy compared to the Euclidean distance-based approach. Finally, a study on comic style is provided along with a learning model with an accuracy of 0.89 to analyse the reading order of comics.

Significance StatementKey differences exist between how rural and urban people receive benefits from nature (termed ecosystem services; ES). In rural areas, people are thought to have relatively direct relationships with local ecosystems (e.g. growing food on your subsistence farm). By contrast, within urban areas, people often have more indirect access to distant ecosystems (e.g. obtaining food from hundreds of miles away via supermarket value chain). However, this leaves many questions unanswered: e.g., What natural benefits are present within cities? When do nature’s benefits flow into cities? When do the people travel out to directly receive nature’s benefits? Here, we explore this issue – breaking down ES flows into two components (i.e. the movement of natural goods and the movement of beneficiaries [people]).

In 2006, the world’s population passed the threshold of being equally split between rural and urban areas. Since this point, urbanisation has continued, and the majority of the global population are now urban inhabitants. With this ongoing change, it is likely that the way people receive benefits from nature (ecosystem services, ES) has also evolved. Environmental theory suggests that rural residents depend directly on their local environment (conceptualised as green-loop systems), whereas urban residents have relatively indirect relationships with distant ecosystems (conceptualised as red-loop systems). Here, we evaluate this theory using survey data from &gt, 3000 households in and around Hyderabad, India. Controlling for other confounding socioeconomic variables, we investigate how flows of 10 ES vary across rural, peri-urban and urban areas. For most of the ES we investigated, we found no statistical differences in the levels of direct or indirect use of an ecosystem, the distance to the ecosystem, nor the quantities of ES used between rural and urban residents (p &gt, 0.05). However, our results do show that urban people themselves often travel shorter distances than rural people to access most ES, likely because improved infrastructure in urban areas allows for the transport of ES from wider ecosystems to the locality of the beneficiaries’ place of residence. Thus, while we find some evidence to support red-loop–green-loop theory, we conclude that ES flows across the rural-urban spectrum may show more similarities than might be expected. As such, the impact of future urbanisation on ES flows may be limited, because many flows in both rural and urban areas have already undergone globalisation.

Recent development in NLP shows a strong trend towards refining pre-trained models with a domain-specific dataset. This is especially the case for response generation where emotion plays an important role. However, existing empathetic datasets remain small, delaying research efforts in this area, for example, the development of emotion-aware chatbots. One main technical challenge has been the cost of manually annotating dialogues with the right emotion labels. In this paper, we describe a large-scale silver dataset consisting of 1M dialogues annotated with 32 fine-grained emotions, eight empathetic response intents, and the Neutral category. To achieve this goal, we have developed a novel data curation pipeline starting with a small seed of manually annotated data and eventually scaling it to a satisfactory size. We compare its quality against a state-of-the-art gold dataset using offline experiments and visual validation methods. The resultant procedure can be used to create similar datasets in the same domain as well as in other domains.(1)

Providing safe sanitation in the developing world is still a major hurdle to achieving Sustainable Development Goal number six, with 61% of the global population lacking safely managed sanitation services. Circular economy in the context of sanitation focuses on the whole sanitation chain which includes the provision of toilets, the collection of waste, treatment and transformation into sanitation-derived products including fertiliser, fuel and clean water. As well as potentially reducing the cost of toilet provision, a circular economy approach also has the potential to enable positive environmental and health impacts, unlike other systems where waste may be discharged untreated into the environment. The implementation of a system level transformation is not simple, considering operator capacity, lack of funding, slowly growing acceptance by local communities, and a policy landscape which can be inconsistent in its support for the circular economy. As India invests in long-term infrastructure to improve citizens’ quality of life (e.g., Swachh Bharat Mission), it could incorporate circular economy principles into the design of infrastructure, creating effective urban nutrient and material cycles, enhancing economic development and welfare. This represents a significant opportunity for government and businesses in India to develop circular sanitation infrastructure to recover and valorise biological nutrients. After collecting information from five case studies across India, covering different treatment technologies, waste-derived products, markets and contexts; this research identifies the main barriers and enablers for circular sanitation business models to succeed. Whilst there were many different institutional and technological arrangements, common issues of managing and enforcing incoming waste and competing with chemical fertilisers were found.